Fundamentals of Deep Learning

This chapter covers the foundational concepts of deep learning, starting with an overview of neural networks and key concepts such as neurons, layers, and activation functions. It explains different types of training stages, including forward and backward propagation, and the role of loss functions in minimizing errors. Key optimization techniques, such as the gradient descent algorithm, weight initialization, and regularization, are discussed.

The chapter also focuses on hyperparameter tuning, exploring architecture- and training-based hyperparameters, and explaining kernel size, learning rate, weight decay, dropout, and batch settings. Finally, the chapter addresses the challenges associated with hyperparameter tuning and discusses best practices for optimizing training process.